{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from modelscope import snapshot_download\n",
    "model_name = \"unsloth/Qwen3-14B\"\n",
    "model_dir = snapshot_download(model_name, cache_dir=\"qwen3\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from datasets import load_dataset,Dataset\n",
    "import subprocess\n",
    "import os\n",
    "\n",
    "result = subprocess.run('bash -c \"source /etc/network_turbo && env | grep proxy\"', shell=True, capture_output=True, text=True)\n",
    "output = result.stdout\n",
    "for line in output.splitlines():\n",
    "    if '=' in line:\n",
    "        var, value = line.split('=', 1)\n",
    "        os.environ[var] = value\n",
    "\n",
    "from huggingface_hub import notebook_login\n",
    "notebook_login()\n",
    "# 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\n",
    "\n",
    "from datasets import load_dataset\n",
    "reasoning_dataset = load_dataset(\"unsloth/OpenMathReasoning-mini\", split = \"cot\", cache_dir = 'data')\n",
    "non_reasoning_dataset = load_dataset(\"mlabonne/FineTome-100k\", split = \"train\", cache_dir = 'data')"
   ],
   "id": "609371e57f86f7a0"
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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